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542. A great anniversary of the General Commercial Code
- Creator:
- Skřejpková, Petra
- Type:
- article, model:article, and TEXT
- Language:
- English
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
543. A grid-computing based multi-camera tracking system for vehicle plate recognition
- Creator:
- Musa, Zalili Binti and Watada, Junzo
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- vehicle plate recognition, grid computing, recognition system, and tracking system
- Language:
- English
- Description:
- There are several ways that can be implemented in a vehicle tracking system such as recognizing a vehicle color, a shape or a vehicle plate itself. In this paper, we will concentrate ourselves on recognizing a vehicle on a highway through vehicle plate recognition. Generally, recognizing a vehicle plate for a toll-gate system or parking system is easier than recognizing a car plate for the highway system. There are many cameras installed on the highway to capture images and every camera has different angles of images. As a result, the images are captured under varied imaging conditions and not focusing on the vehicle itself. Therefore, we need a system that is able to recognize the object first. However, such a system consumes a large amount of time to complete the whole process. To overcome this drawback, we installed this process with grid computing as a solution. At the end of this paper, we will discuss our obtained result from an experiment.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
544. A gut-specific chitinase from the mulberry longicorn beetle, Apriona germari (Coleoptera: Cerambycidae): cDNA cloning, gene structure, expression and enzymatic activity
- Creator:
- Choo, Young Moo, Lee, Kwang Sik, Kim, Bo Yeon, Kim, Doh Hoon, Yoon, Hyung Joo, Sohn, Hung Dae, and Jin, Byung Rae
- Type:
- article, model:article, and TEXT
- Subject:
- Cerambycidae, Apriona germari, baculovirus expression vector, cDNA cloning, chitinase, enzyme, gene structure, and mulberry longicorn beetle
- Language:
- English
- Description:
- A gut-specific chitinase gene was cloned from the mulberry longicorn beetle, Apriona germari. The A. germari chitinase (AgChi) gene spans 2894 bp and consists of five introns and six exons coding for 390 amino acid residues. AgChi possesses the chitinase family 18 active site signature and three N-glycosylation sites. Southern blot analysis of genomic DNA suggests that AgChi is a single copy gene. The AgChi cDNA was expressed as a 46-kDa polypeptide in baculovirus-infected insect Sf9 cells and the recombinant AgChi showed activity in a chitinase enzyme assay. Treatment of recombinant virus-infected Sf9 cells with tunicamycin, a specific inhibitor of N-linked glycosylation, revealed that AgChi is N-glycosylated, but the carbohydrate moieties are not essential for chitinolytic activity. Northern and Western blot analyses showed that AgChi was specifically expressed in the gut; AgChi was expressed in three gut regions, indicating that the gut is the prime site for AgChi synthesis in A. germari larvae.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
545. A gypsy moth (Lymantria dispar, Lepidoptera: Lymantriidae) multinucleocapsid nuclear polyhedrosis virus from France: comparison with a North American and a Korean strain
- Creator:
- Narang, Neelam, Hérard, Franck, Dougherty, Edward M., Chen, Kim, and Vega, Fernardo E.
- Type:
- article, model:article, and TEXT
- Subject:
- Biocontrol, microbial control, insect pathogens, baculovirus, NPV, nucleopolyhedrovirus, gypsy moth, Lymantria dispar, and fluorescent brightener
- Language:
- English
- Description:
- As part of a search for natural enemies of the gypsy moth (Lymantria dispar), virus-infected samples were collected near Toulouse, France. Light and electron microscope studies confirmed that the French strain is a multinucleocapsid nuclear polyhedrosis virus (MNPV). In vivo bioassays using the New Jersey strain of L. dispar, and comparing L. dispar MNPV (LdMNPV) strains from France, North America and Korea, showed that the French strain was the least active, whereas the North American strain had the highest activity. The viral efficacy of all strains was enhanced 200 to 1300-fold in the presence of 1% fluorescent brightener. The enhancement was highest in the American strain and lowest in the French strain. French LdMNPV (LdMNPVF) DNA cut with four restriction enzymes (BamHI, EcoRI, HindIII, and NotI) revealed minor fragment size differences, but many similarities when compared to the North American and the Korean strain. PCR amplification of a LdMNPV early gene (G22) was detected in the North American and the Korean strain, but not in the French strain.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
546. A Havel-Hakimi type procedure and a sufficient condition for a sequence to be potentially $S_{r,s}$-graphic
- Creator:
- Yin, Jian-Hua
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- graph, split graph, and degree sequence
- Language:
- English
- Description:
- The split graph $K_r+\overline {K_s}$ on $r+s$ vertices is denoted by $S_{r,s}$. A non-increasing sequence $\pi =(d_1,d_2,\ldots ,d_n)$ of nonnegative integers is said to be potentially $S_{r,s}$-graphic if there exists a realization of $\pi $ containing $S_{r,s}$ as a subgraph. In this paper, we obtain a Havel-Hakimi type procedure and a simple sufficient condition for $\pi $ to be potentially $S_{r,s}$-graphic. They are extensions of two theorems due to A. R. Rao (The clique number of a graph with given degree sequence, Graph Theory, Proc. Symp., Calcutta 1976, ISI Lect. Notes Series 4 (1979), 251–267 and An Erdős-Gallai type result on the clique number of a realization of a degree sequence, unpublished).
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
547. A heuristic and its mathematical analogue within artificial neural network adaptation context
- Creator:
- Serpen, Gursel
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- Hopfield network, adaptation methods, heuristic methods, and gradient descent
- Language:
- English
- Description:
- This paper presents an observation on adaptation of Hopfield neural network dynamics configured as a relaxation-based search algorithm for static optimization. More specifically, two adaptation rules, one heuristically formulated and the second being gradient descent based, for updating constraint weighting coefficients of Hopfield neural network dynamics are discussed. Application of two adaptation rules for constraint weighting coefficients is shown to lead to an identical form for update equations. This finding suggests that the heuristically-formulated rule and the gradient descent based rule are analogues of each other. Accordingly, in the current context, common sense reasoning by a domain expert appears to possess a corresponding mathematical framework.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
548. A hierarchical artificial neural network for transport energy demand forecast: Iran case study
- Creator:
- Kazemi, Aliyeh , Shakouri , Hamed G., Menhaj, M. Bagher., Mehregan, Reza, and Neshal, Najmeh
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- ANNs, MLP, BP algorithm, forecasting, and transport energy demand
- Language:
- English
- Description:
- This paper presents a neuro-based approach for annual transport energy demand forecasting by several socio-economic indicators. In order to analyze the influence of economic and social indicators on the transport energy demand, gross domestic product (GDP), population and total number of vehicles are selected. This approach is structured as a hierarchical artificial neural networks (ANNs) model based on the supervised multi-layer perceptron (MLP), trained with the back-propagation (BP) algorithm. This hierarchical ANNs model is designed properly. The input variables are transport energy demand in the last year, GDP, population and total number of vehicles. The output variable is the energy demand of the transportation sector in Million Barrels Oil Equivalent (MBOE). This paper proposes a hierarchical artificial neural network by which the inputs to the ending level are obtained as outputs of the starting levels. Actual data of Iran from 1968-2007 is used to train the hierarchical ANNs and to illustrate capability of the approach in this regard. Comparison of the model predictions with conventional regression model predictions shows its superiority. Furthermore, the transport energy demand of Iran for the period of 2008 to 2020 is estimated.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
549. A high speed back propagation neural network for multistage MR brain tumor image segmentation
- Creator:
- Hemanth , D. Jude, Vijila, C. Kezi Selva, and Anitha, J.
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- Back propagation, neural network, MR brain image, high speed BPN, and convergence time period
- Language:
- English
- Description:
- Artificial neural networks (ANN) are one of the highly preferred artificial intelligence techniques for brain image segmentation. The commonly used ANN is the supervised ANN, namely Back Propagation Neural Network (BPN). Even though BPNs guarantee high efficiency, they are computationally non-feasible due to the huge convergence time period. In this work, the aspect of computational complexity is tackled using the proposed high speed BPN algorithm (HSBPN). In this modified approach, the weight vectors are calculated without any training methodology. Magnetic resonance (MR) brain tumor images of three stages, namely severe, moderate and mild, are used in this work. An extensive feature set is extracted from these images and used as input for the neural network. A comparative analysis is performed between the conventional BPN and the HSBPN in terms of convergence time period and segmentation efficiency. Experimental results show the superior nature of HSBPN in terms of the performance measures.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
550. A High-accuracy Self-adaptive Resource Demands Predicting Method in IaaS Cloud Environment
- Creator:
- Chen , Z., Zhu , Y., Di , Y., Feng, S., and Geng , J.
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- IaaS cloud enviroment, user demandprediction, high-accuracy self-adaptive prediction, BP neural network, self-adjusting learning rate and momentum, and static evaluation
- Language:
- English
- Description:
- In IaaS (Infrastructure as a Service) cloud environment, users are provisioned with virtual machines (VMs). However, the initialization and resource allocation of virtual machines are not instantaneous and usually minutes of time are needed. Therefore, to realize efficient resource provision, it is necessary to know the accurate amount of resources needed to be allocated in advance. For this purpose, this paper proposes a high-accuracy self-adaptive prediction method using optimized neural network. The characters of users demands and preferences are analyzed firstly. To deal with the specific circumstances, a dynamic self-adaptive prediction model is adopted. Some basic predictors are adopted for resource requirements prediction of simple circumstances. BP neural network with self-adjusting learning rate and momentum is adopted to optimize the prediction results. High-accuracy self-adaptive prediction is realized by using the prediction results of basic predictors with different weights as training data besides the historical data. Feedback control is introduced to improve the whole operation performance. Statistic validation of the method is conducted adopting multiple evaluation criteria. The experiment results show that the method is promising for effectively predicting resource requirements in the cloud environment.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public